Merge pull request #3444 from microsoft/v2.1

V2.1 merge back to master

nni/retiarii/experiment/pytorch.py

+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+
 import atexit
 import logging
 import time

nni/retiarii/graph.py

+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+
 """
 Model representation.
 """

nni/retiarii/integration.py

+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+
 import logging
 import os
 from typing import Any, Callable
@@ -99,7 +102,7 @@ class RetiariiAdvisor(MsgDispatcherBase):
             'parameters': parameters,
             'parameter_source': 'algorithm'
         }
-        _logger.info('New trial sent: %s', new_trial)
+        _logger.debug('New trial sent: %s', new_trial)
         send(CommandType.NewTrialJob, json_dumps(new_trial))
         if self.send_trial_callback is not None:
             self.send_trial_callback(parameters)  # pylint: disable=not-callable
@@ -109,21 +112,21 @@ class RetiariiAdvisor(MsgDispatcherBase):
         send(CommandType.NoMoreTrialJobs, '')
 
     def handle_request_trial_jobs(self, num_trials):
-        _logger.info('Request trial jobs: %s', num_trials)
+        _logger.debug('Request trial jobs: %s', num_trials)
         if self.request_trial_jobs_callback is not None:
             self.request_trial_jobs_callback(num_trials)  # pylint: disable=not-callable
 
     def handle_update_search_space(self, data):
-        _logger.info('Received search space: %s', data)
+        _logger.debug('Received search space: %s', data)
         self.search_space = data
 
     def handle_trial_end(self, data):
-        _logger.info('Trial end: %s', data)
+        _logger.debug('Trial end: %s', data)
         self.trial_end_callback(json_loads(data['hyper_params'])['parameter_id'],  # pylint: disable=not-callable
                                 data['event'] == 'SUCCEEDED')
 
     def handle_report_metric_data(self, data):
-        _logger.info('Metric reported: %s', data)
+        _logger.debug('Metric reported: %s', data)
         if data['type'] == MetricType.REQUEST_PARAMETER:
             raise ValueError('Request parameter not supported')
         elif data['type'] == MetricType.PERIODICAL:

nni/retiarii/integration_api.py

+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+
 import json
 from typing import NewType, Any
 

nni/retiarii/mutator.py

+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+
 from typing import (Any, Iterable, List, Optional)
 
 from .graph import Model

nni/retiarii/nn/pytorch/api.py

+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+
 from collections import OrderedDict
 from typing import Any, List, Union, Dict
 import warnings

nni/retiarii/nn/pytorch/mutator.py

+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+
 from typing import Any, List, Optional, Tuple
 
 from ...mutator import Mutator

nni/retiarii/nn/pytorch/nn.py

+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+
 import torch
 import torch.nn as nn
 

nni/retiarii/oneshot/__init__.py

+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+
 from .interface import BaseOneShotTrainer

nni/retiarii/oneshot/interface.py

+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+
 import abc
 from typing import Any
 

nni/retiarii/oneshot/pytorch/__init__.py

+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+
 from .darts import DartsTrainer
 from .enas import EnasTrainer
 from .proxyless import ProxylessTrainer

nni/retiarii/operation.py

+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+
 from typing import (Any, Dict, List)
 
 from . import debug_configs

nni/retiarii/operation_def/tf_op_def.py

+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+
 from ..operation import TensorFlowOperation
 
 

nni/retiarii/operation_def/torch_op_def.py

+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+
 from typing import (Any, List)
 
 import torch
@@ -369,7 +372,6 @@ class TensorOps(PyTorchOperation):
             return TensorOpExceptions[self.type](output, inputs)
         op_name = self.type.split('::')[-1]
         args_str = ', '.join([f'{name}={inputs[i+1]}' for i, (name, t, default) in enumerate(matched_args)])
-        print(args_str)
         return f'{output} = {inputs[0]}.{op_name}({args_str})'
 
 class TorchOps(PyTorchOperation):

nni/retiarii/serializer.py

+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+
 import abc
 import functools
 import inspect

nni/retiarii/strategy/__init__.py

+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+
 from .base import BaseStrategy
 from .bruteforce import Random, GridSearch
 from .evolution import RegularizedEvolution

nni/retiarii/strategy/base.py

+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+
 import abc
 from typing import List
 

nni/retiarii/strategy/bruteforce.py

+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+
 import copy
 import itertools
 import logging
@@ -36,7 +39,7 @@ def random_generator(search_space: Dict[Any, List[Any]], dedup=True, retries=500
                 history.add(selected)
                 break
             if retry_count + 1 == retries:
-                _logger.info('Random generation has run out of patience. There is nothing to search. Exiting.')
+                _logger.debug('Random generation has run out of patience. There is nothing to search. Exiting.')
                 return
         yield {key: value for key, value in zip(keys, selected)}
 
@@ -58,7 +61,7 @@ class GridSearch(BaseStrategy):
     def run(self, base_model, applied_mutators):
         search_space = dry_run_for_search_space(base_model, applied_mutators)
         for sample in grid_generator(search_space, shuffle=self.shuffle):
-            _logger.info('New model created. Waiting for resource. %s', str(sample))
+            _logger.debug('New model created. Waiting for resource. %s', str(sample))
             if query_available_resources() <= 0:
                 time.sleep(self._polling_interval)
             submit_models(get_targeted_model(base_model, applied_mutators, sample))
@@ -101,7 +104,7 @@ class Random(BaseStrategy):
                     model = base_model
                     for mutator in applied_mutators:
                         model = mutator.apply(model)
-                    _logger.info('New model created. Applied mutators are: %s', str(applied_mutators))
+                    _logger.debug('New model created. Applied mutators are: %s', str(applied_mutators))
                     submit_models(model)
                 else:
                     time.sleep(self._polling_interval)
@@ -109,7 +112,7 @@ class Random(BaseStrategy):
             _logger.info('Random search running in fixed size mode. Dedup: %s.', 'on' if self.dedup else 'off')
             search_space = dry_run_for_search_space(base_model, applied_mutators)
             for sample in random_generator(search_space, dedup=self.dedup):
-                _logger.info('New model created. Waiting for resource. %s', str(sample))
+                _logger.debug('New model created. Waiting for resource. %s', str(sample))
                 if query_available_resources() <= 0:
                     time.sleep(self._polling_interval)
                 submit_models(get_targeted_model(base_model, applied_mutators, sample))

nni/retiarii/strategy/evolution.py

+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+
 import collections
 import dataclasses
 import logging
@@ -122,7 +125,7 @@ class RegularizedEvolution(BaseStrategy):
                 break
 
     def _submit_config(self, config, base_model, mutators):
-        _logger.info('Model submitted to running queue: %s', config)
+        _logger.debug('Model submitted to running queue: %s', config)
         model = get_targeted_model(base_model, mutators, config)
         submit_models(model)
         self._running_models.append((config, model))
@@ -138,7 +141,7 @@ class RegularizedEvolution(BaseStrategy):
                 metric = model.metric
             if metric is not None:
                 individual = Individual(config, metric)
-                _logger.info('Individual created: %s', str(individual))
+                _logger.debug('Individual created: %s', str(individual))
                 self._population.append(individual)
                 if len(self._population) > self.population_size:
                     self._population.popleft()

nni/retiarii/strategy/tpe_strategy.py

+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+
 import logging
 import time
 
@@ -55,7 +58,7 @@ class TPEStrategy(BaseStrategy):
             avail_resource = query_available_resources()
             if avail_resource > 0:
                 model = base_model
-                _logger.info('New model created. Applied mutators: %s', str(applied_mutators))
+                _logger.debug('New model created. Applied mutators: %s', str(applied_mutators))
                 self.tpe_sampler.generate_samples(self.model_id)
                 for mutator in applied_mutators:
                     mutator.bind_sampler(self.tpe_sampler)